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Del Mauro G, Li Y, Wang Z. Global brain connectivity: Test-retest stability and association with biological and neurocognitive variables. J Neurosci Methods 2024; 409:110205. [PMID: 38914376 DOI: 10.1016/j.jneumeth.2024.110205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 06/03/2024] [Accepted: 06/21/2024] [Indexed: 06/26/2024]
Abstract
BACKGROUND Global brain connectivity (GBC) enables measuring brain regions' functional connectivity strength at rest by computing the average correlation between each brain voxel's time-series and that of all other voxels. NEW METHOD We used resting-state fMRI (rs-fMRI) data of young adult participants from the Human Connectome Project (HCP) dataset to explore the test-retest stability of GBC, the brain regions with higher or lower GBC, as well as the associations of this measure with age, sex, and fluid intelligence. GBC was computed by considering separately the positive and negative correlation coefficients (positive GBC and negative GBC). RESULTS Test-retest stability was higher for positive compared to negative GBC. Areas with higher GBC were located in the default mode network, insula, and visual areas, while regions with lower GBC were in subcortical regions, temporal cortex, and cerebellum. Higher age was related to global reduction of positive GBC. Males displayed higher positive GBC in the whole brain. Fluid intelligence was associated to increased positive GBC in fronto-parietal, occipital and temporal regions. COMPARISON WITH EXISTING METHOD Compared to previous works, this study adopted a larger sample size and tested GBC stability using data from different rs-fMRI sessions. Moreover, these associations were examined by testing positive and negative GBC separately. CONCLUSIONS Lower stability for negative compared to positive GBC suggests that negative correlations may reflect less stable couplings between brain regions. Our findings indicate a greater importance of positive compared to negative GBC for the associations of functional connectivity strength with biological and neurocognitive variables.
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Affiliation(s)
- Gianpaolo Del Mauro
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, 670 W Baltimore St, HSF III, Baltimore, MD 21202, United States
| | - Yiran Li
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, 670 W Baltimore St, HSF III, Baltimore, MD 21202, United States
| | - Ze Wang
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, 670 W Baltimore St, HSF III, Baltimore, MD 21202, United States.
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Zhen Y, Yang Y, Zheng Y, Wang X, Liu L, Zheng Z, Zheng H, Tang S. The heritability and structural correlates of resting-state fMRI complexity. Neuroimage 2024; 296:120657. [PMID: 38810892 DOI: 10.1016/j.neuroimage.2024.120657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Revised: 05/24/2024] [Accepted: 05/26/2024] [Indexed: 05/31/2024] Open
Abstract
The complexity of fMRI signals quantifies temporal dynamics of spontaneous neural activity, which has been increasingly recognized as providing important insights into cognitive functions and psychiatric disorders. However, its heritability and structural underpinnings are not well understood. Here, we utilize multi-scale sample entropy to extract resting-state fMRI complexity in a large healthy adult sample from the Human Connectome Project. We show that fMRI complexity at multiple time scales is heritable in broad brain regions. Heritability estimates are modest and regionally variable. We relate fMRI complexity to brain structure including surface area, cortical myelination, cortical thickness, subcortical volumes, and total brain volume. We find that surface area is negatively correlated with fine-scale complexity and positively correlated with coarse-scale complexity in most cortical regions, especially the association cortex. Most of these correlations are related to common genetic and environmental effects. We also find positive correlations between cortical myelination and fMRI complexity at fine scales and negative correlations at coarse scales in the prefrontal cortex, lateral temporal lobe, precuneus, lateral parietal cortex, and cingulate cortex, with these correlations mainly attributed to common environmental effects. We detect few significant associations between fMRI complexity and cortical thickness. Despite the non-significant association with total brain volume, fMRI complexity exhibits significant correlations with subcortical volumes in the hippocampus, cerebellum, putamen, and pallidum at certain scales. Collectively, our work establishes the genetic basis and structural correlates of resting-state fMRI complexity across multiple scales, supporting its potential application as an endophenotype for psychiatric disorders.
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Affiliation(s)
- Yi Zhen
- School of Mathematical Sciences, Beihang University, Beijing 100191, China; Key laboratory of Mathematics, Informatics and Behavioral Semantics, Beihang University, Beijing 100191, China
| | - Yaqian Yang
- School of Mathematical Sciences, Beihang University, Beijing 100191, China; Key laboratory of Mathematics, Informatics and Behavioral Semantics, Beihang University, Beijing 100191, China
| | - Yi Zheng
- School of Mathematical Sciences, Beihang University, Beijing 100191, China; Key laboratory of Mathematics, Informatics and Behavioral Semantics, Beihang University, Beijing 100191, China
| | - Xin Wang
- Institute of Artificial Intelligence, Beihang University, Beijing 100191, China; Key laboratory of Mathematics, Informatics and Behavioral Semantics, Beihang University, Beijing 100191, China; Zhongguancun Laboratory, Beijing 100094, China; Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing 100191, China; PengCheng Laboratory, Shenzhen 518055, China
| | - Longzhao Liu
- Institute of Artificial Intelligence, Beihang University, Beijing 100191, China; Key laboratory of Mathematics, Informatics and Behavioral Semantics, Beihang University, Beijing 100191, China; Zhongguancun Laboratory, Beijing 100094, China; Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing 100191, China; PengCheng Laboratory, Shenzhen 518055, China
| | - Zhiming Zheng
- Institute of Artificial Intelligence, Beihang University, Beijing 100191, China; Key laboratory of Mathematics, Informatics and Behavioral Semantics, Beihang University, Beijing 100191, China; Institute of Medical Artificial Intelligence, Binzhou Medical University, Yantai 264003, China; Zhongguancun Laboratory, Beijing 100094, China; Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing 100191, China; PengCheng Laboratory, Shenzhen 518055, China; State Key Lab of Software Development Environment, Beihang University, Beijing 100191, China
| | - Hongwei Zheng
- Beijing Academy of Blockchain and Edge Computing, Beijing 100085, China.
| | - Shaoting Tang
- Institute of Artificial Intelligence, Beihang University, Beijing 100191, China; Key laboratory of Mathematics, Informatics and Behavioral Semantics, Beihang University, Beijing 100191, China; Institute of Medical Artificial Intelligence, Binzhou Medical University, Yantai 264003, China; Zhongguancun Laboratory, Beijing 100094, China; Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing 100191, China; PengCheng Laboratory, Shenzhen 518055, China; State Key Lab of Software Development Environment, Beihang University, Beijing 100191, China.
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Del Mauro G, Sevel LS, Boissoneault J, Wang Z. Divergent association between pain intensity and resting-state fMRI-based brain entropy in different age groups. J Neurosci Res 2024; 102:e25341. [PMID: 38751218 PMCID: PMC11154588 DOI: 10.1002/jnr.25341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 04/24/2024] [Accepted: 04/27/2024] [Indexed: 06/11/2024]
Abstract
Pain is a multidimensional subjective experience sustained by multiple brain regions involved in different aspects of pain experience. We used brain entropy (BEN) estimated from resting-state fMRI (rsfMRI) data to investigate the neural correlates of pain experience. BEN was estimated from rs-fMRI data provided by two datasets with different age range: the Human Connectome Project-Young Adult (HCP-YA) and the Human Connectome project-Aging (HCP-A) datasets. Retrospective assessment of experienced pain intensity was retrieved from both datasets. No main effect of pain intensity was observed. The interaction between pain and age, however, was related to increased BEN in several pain-related brain regions, reflecting greater variability of spontaneous brain activity. Dividing the sample into a young adult group (YG) and a middle age-aging group (MAG) resulted in two divergent patterns of pain-BEN association: In the YG, pain intensity was related to reduced BEN in brain regions involved in the sensory processing of pain; in the MAG, pain was associated with increased BEN in areas related to both sensory and cognitive aspects of pain experience.
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Affiliation(s)
- Gianpaolo Del Mauro
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Landrew Samuel Sevel
- Department of Anesthesiology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Jeff Boissoneault
- Department of Anesthesiology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Ze Wang
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
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Lin C, Lee SH, Huang CM, Wu YW, Chang YX, Liu HL, Ng SH, Cheng YC, Chiu CC, Wu SC. Cognitive protection and brain entropy changes from omega-3 polyunsaturated fatty acids supplement in late-life depression: A 52-week randomized controlled trial. J Affect Disord 2024; 351:15-23. [PMID: 38281596 DOI: 10.1016/j.jad.2024.01.205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 01/19/2024] [Accepted: 01/22/2024] [Indexed: 01/30/2024]
Abstract
BACKGROUND Late-life depression (LLD) is associated with risk of dementia, yet intervention of LLD provides an opportunity to attenuate subsequent cognitive decline. Omega-3 polyunsaturated fatty acids (PUFAs) supplement is a potential intervention due to their beneficial effect in depressive symptoms and cognitive function. To explore the underlying neural mechanism, we used resting-state functional MRI (rs-fMRI) before and after omega-3 PUFAs supplement in older adults with LLD. METHODS A 52-week double-blind randomized controlled trial was conducted. We used multi-scale sample entropy to analyze rs-fMRI data. Comprehensive cognitive tests and inflammatory markers were collected to correlate with brain entropy changes. RESULTS A total of 20 patients completed the trial with 11 under omega-3 PUFAs and nine under placebo. While no significant global cognitive improvement was observed, a marginal enhancement in processing speed was noted in the omega-3 PUFAs group. Importantly, participants receiving omega-3 PUFAs exhibited decreased brain entropy in left posterior cingulate gyrus (PCG), multiple visual areas, the orbital part of the right middle frontal gyrus, and the left Rolandic operculum. The brain entropy changes of the PCG in the omega-3 PUFAs group correlated with improvement of language function and attenuation of interleukin-6 levels. LIMITATIONS Sample size is small with only marginal clinical effect. CONCLUSION These findings suggest that omega-3 PUFAs supplement may mitigate cognitive decline in LLD through anti-inflammatory mechanisms and modulation of brain entropy. Larger clinical trials are warranted to validate the potential therapeutic implications of omega-3 PUFAs for deterring cognitive decline in patients with late-life depression.
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Affiliation(s)
- Chemin Lin
- Department of Psychiatry, Keelung Chang Gung Memorial Hospital, Keelung City, Taiwan; College of Medicine, Chang Gung University, Taoyuan County, Taiwan.; Community Medicine Research Center, Chang Gung Memorial Hospital, Keelung, Keelung, Taiwan
| | - Shwu-Hua Lee
- College of Medicine, Chang Gung University, Taoyuan County, Taiwan.; Department of Psychiatry, Linkou Chang Gung Memorial Hospital, Taoyuan County, Taiwan
| | - Chih-Mao Huang
- Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Yu-Wen Wu
- Department of Engineering and System Science, National Tsing Hua University, Hsinchu, Taiwan
| | - You-Xun Chang
- Department of Engineering and System Science, National Tsing Hua University, Hsinchu, Taiwan
| | - Ho-Ling Liu
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX, United States of America
| | - Shu-Hang Ng
- Department of Head and Neck Oncology Group, Linkou Chang Gung Memorial Hospital and Chang Gung University, Taoyuan, Taiwan; Department of Diagnostic Radiology, Linkou Chang Gung Memorial Hospital and Chang Gung University, Taoyuan, Taiwan
| | - Ying-Chih Cheng
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan; Department of Psychiatry, China Medical University Hsinchu Hospital, China Medical University, Hsinchu, Taiwan; Research Center of Big Data and Meta-analysis, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
| | - Chih-Chiang Chiu
- Department of Psychiatry, Taipei City Psychiatric Center, Taipei City Hospital, Taipei, Taiwan; Department of Psychiatry, Taipei Medical University, Taipei, Taiwan.
| | - Shun-Chi Wu
- Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
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Xin X, Yu J, Gao X. The brain entropy dynamics in resting state. Front Neurosci 2024; 18:1352409. [PMID: 38595975 PMCID: PMC11002175 DOI: 10.3389/fnins.2024.1352409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 03/08/2024] [Indexed: 04/11/2024] Open
Abstract
As a novel measure for irregularity and complexity of the spontaneous fluctuations of brain activities, brain entropy (BEN) has attracted much attention in resting-state functional magnetic resonance imaging (rs-fMRI) studies during the last decade. Previous studies have shown its associations with cognitive and mental functions. While most previous research assumes BEN is approximately stationary during scan sessions, the brain, even at its resting state, is a highly dynamic system. Such dynamics could be characterized by a series of reoccurring whole-brain patterns related to cognitive and mental processes. The present study aims to explore the time-varying feature of BEN and its potential links with general cognitive ability. We adopted a sliding window approach to derive the dynamical brain entropy (dBEN) of the whole-brain functional networks from the HCP (Human Connectome Project) rs-fMRI dataset that includes 812 young healthy adults. The dBEN was further clustered into 4 reoccurring BEN states by the k-means clustering method. The fraction window (FW) and mean dwell time (MDT) of one BEN state, characterized by the extremely low overall BEN, were found to be negatively correlated with general cognitive abilities (i.e., cognitive flexibility, inhibitory control, and processing speed). Another BEN state, characterized by intermediate overall BEN and low within-state BEN located in DMN, ECN, and part of SAN, its FW, and MDT were positively correlated with the above cognitive abilities. The results of our study advance our understanding of the underlying mechanism of BEN dynamics and provide a potential framework for future investigations in clinical populations.
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Affiliation(s)
- Xiaoyang Xin
- Center for Psychological Sciences, Zhejiang University, Hangzhou, China
- Preschool College, Luoyang Normal University, Luoyang, China
| | - Jiaqian Yu
- Center for Psychological Sciences, Zhejiang University, Hangzhou, China
| | - Xiaoqing Gao
- Center for Psychological Sciences, Zhejiang University, Hangzhou, China
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Stobbe E, Forlim CG, Kühn S. Impact of exposure to natural versus urban soundscapes on brain functional connectivity, BOLD entropy and behavior. ENVIRONMENTAL RESEARCH 2024; 244:117788. [PMID: 38040180 DOI: 10.1016/j.envres.2023.117788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 11/21/2023] [Accepted: 11/23/2023] [Indexed: 12/03/2023]
Abstract
BACKGROUND Humans have been moving from rural to urban environments for decades. This process may have important consequences for our health and well-being. Most previous studies have focused on visual input, and the auditory domain has been understudied so far. Therefore, we set out to investigate the influence of exposure to natural vs urban soundscapes on brain activity and behavior. METHODS Resting-state fMRI data was acquired while participants (N = 35) listened to natural and urban soundscapes. Two affective questionnaires (the Positive and Negative Affect Schedule (PANAS) and the Perceived Stress Scale) and two cognitive tasks (dual n-back (DNB) and the backward digit-span (BDS)) were assessed before and after each soundscape condition. To quantify brain function we used complexity and network measures, namely brain entropy (BEN) and whole brain functional connectivity (FC). To study the link between brain and behavior, changes in BEN and whole brain FC were correlated to changes in cognitive performance and self-reported affect. RESULTS We found higher BEN when listening to urban sounds in posterior cingulate gyrus, cuneus and precuneus, occipital lobe/calcarine as compared to nature sounds, which was negatively correlated to (post-pre) differences in positive affect (PANAS) in the urban soundscape condition. In addition, we found higher FC between areas in the auditory, cinguloopercular, somatomotor hand and mouth networks when listening to nature as compared to urban sounds which was positively correlated to (post-pre) differences of the of the composite score of Digit span and N-back for nature soundscape. CONCLUSIONS This study provides a framework for the neural underpinnings of how natural versus urban soundscapes affect both whole brain FC and BEN and bear implications for the understanding of how the physical auditory environment affects brain function and subsequently observed behavior. Moreover, correlations with cognition and affect reveal the meaning that exposure to soundscapes may have on the human brain. To the best of our knowledge this is the first study to analyze BEN and whole brain FC at rest during exposure to nature and urban soundscapes and to explore their relationship to behavior.
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Affiliation(s)
- Emil Stobbe
- Max Planck Institute for Human Development, Lise Meitner Group for Environmental Neuroscience, Lentzeallee 94, 14195, Berlin, Germany.
| | - Caroline Garcia Forlim
- Max Planck Institute for Human Development, Lise Meitner Group for Environmental Neuroscience, Lentzeallee 94, 14195, Berlin, Germany; University Medical Center Hamburg-Eppendorf, Department of Psychiatry and Psychotherapy, Martinistr. 52, 20251, Hamburg, Germany
| | - Simone Kühn
- Max Planck Institute for Human Development, Lise Meitner Group for Environmental Neuroscience, Lentzeallee 94, 14195, Berlin, Germany; Max Planck-UCL Center for Computational Psychiatry and Ageing Research, Germany; University Medical Center Hamburg-Eppendorf, Department of Psychiatry and Psychotherapy, Martinistr. 52, 20251, Hamburg, Germany
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7
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Camargo A, Del Mauro G, Wang Z. Task-induced changes in brain entropy. J Neurosci Res 2024; 102:e25310. [PMID: 38400553 PMCID: PMC10947426 DOI: 10.1002/jnr.25310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 12/21/2023] [Accepted: 02/09/2024] [Indexed: 02/25/2024]
Abstract
Entropy indicates irregularity of a dynamic system, with higher entropy indicating higher irregularity and more transit states. In the human brain, regional brain entropy (BEN) has been increasingly assessed using resting state fMRI (rs-fMRI), while changes of regional BEN during task-based fMRI have been scarcely studied. The purpose of this study is to characterize task-induced regional BEN alterations using the large Human Connectome Project (HCP) data. To control the potential modulation by the block design, BEN of task-fMRI was calculated from the fMRI images acquired during the task conditions only (task BEN) and then compared to BEN of rs-fMRI (resting BEN). Moreover, BEN was separately calculated from the control blocks of the task-fMRI runs (control BEN) and compared to task BEN. Finally, control BEN was compared to resting BEN to test for residual task effects in the control condition. With respect to resting state, task performance unanimously induced BEN reduction in the peripheral cortical area and BEN increase in the centric part of the sensorimotor and perception networks. Control compared to resting BEN showed similar entropy alterations, suggesting large residual task effects. Task compared to control BEN was characterized by reduced entropy in occipital, orbitofrontal, and parietal regions.
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Affiliation(s)
- Aldo Camargo
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine
| | - Gianpaolo Del Mauro
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine
| | - Ze Wang
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine
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Peterson M, Braga RM, Floris DL, Nielsen JA. Evidence for a Compensatory Relationship between Left- and Right-Lateralized Brain Networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.08.570817. [PMID: 38106130 PMCID: PMC10723397 DOI: 10.1101/2023.12.08.570817] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
The two hemispheres of the human brain are functionally asymmetric. At the network level, the language network exhibits left-hemisphere lateralization. While this asymmetry is widely replicated, the extent to which other functional networks demonstrate lateralization remains a subject of Investigation. Additionally, it is unknown how the lateralization of one functional network may affect the lateralization of other networks within individuals. We quantified lateralization for each of 17 networks by computing the relative surface area on the left and right cerebral hemispheres. After examining the ecological, convergent, and external validity and test-retest reliability of this surface area-based measure of lateralization, we addressed two hypotheses across multiple datasets (Human Connectome Project = 553, Human Connectome Project-Development = 343, Natural Scenes Dataset = 8). First, we hypothesized that networks associated with language, visuospatial attention, and executive control would show the greatest lateralization. Second, we hypothesized that relationships between lateralized networks would follow a dependent relationship such that greater left-lateralization of a network would be associated with greater right-lateralization of a different network within individuals, and that this pattern would be systematic across individuals. A language network was among the three networks identified as being significantly left-lateralized, and attention and executive control networks were among the five networks identified as being significantly right-lateralized. Next, correlation matrices, an exploratory factor analysis, and confirmatory factor analyses were used to test the second hypothesis and examine the organization of lateralized networks. We found general support for a dependent relationship between highly left- and right-lateralized networks, meaning that across subjects, greater left lateralization of a given network (such as a language network) was linked to greater right lateralization of another network (such as a ventral attention/salience network) and vice versa. These results further our understanding of brain organization at the macro-scale network level in individuals, carrying specific relevance for neurodevelopmental conditions characterized by disruptions in lateralization such as autism and schizophrenia.
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Affiliation(s)
- Madeline Peterson
- Department of Psychology, Brigham Young University, Provo, UT, 84602, USA
| | - Rodrigo M. Braga
- Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Dorothea L. Floris
- Methods of Plasticity Research, Department of Psychology, University of Zurich, Zurich, Switzerland
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands
| | - Jared A. Nielsen
- Department of Psychology, Brigham Young University, Provo, UT, 84602, USA
- Neuroscience Center, Brigham Young University, Provo, UT, 84604, USA
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Guan S, Jiang R, Chen DY, Michael A, Meng C, Biswal B. Multifractal long-range dependence pattern of functional magnetic resonance imaging in the human brain at rest. Cereb Cortex 2023; 33:11594-11608. [PMID: 37851793 DOI: 10.1093/cercor/bhad393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 10/01/2023] [Accepted: 10/02/2023] [Indexed: 10/20/2023] Open
Abstract
Long-range dependence is a prevalent phenomenon in various biological systems that characterizes the long-memory effect of temporal fluctuations. While recent research suggests that functional magnetic resonance imaging signal has fractal property, it remains unknown about the multifractal long-range dependence pattern of resting-state functional magnetic resonance imaging signals. The current study adopted the multifractal detrended fluctuation analysis on highly sampled resting-state functional magnetic resonance imaging scans to investigate long-range dependence profile associated with the whole-brain voxels as specific functional networks. Our findings revealed the long-range dependence's multifractal properties. Moreover, long-term persistent fluctuations are found for all stations with stronger persistency in whole-brain regions. Subsets with large fluctuations contribute more to the multifractal spectrum in the whole brain. Additionally, we found that the preprocessing with band-pass filtering provided significantly higher reliability for estimating long-range dependence. Our validation analysis confirmed that the optimal pipeline of long-range dependence analysis should include band-pass filtering and removal of daily temporal dependence. Furthermore, multifractal long-range dependence characteristics in healthy control and schizophrenia are different significantly. This work has provided an analytical pipeline for the multifractal long-range dependence in the resting-state functional magnetic resonance imaging signal. The findings suggest differential long-memory effects in the intrinsic functional networks, which may offer a neural marker finding for understanding brain function and pathology.
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Affiliation(s)
- Sihai Guan
- College of Electronic and Information, Southwest Minzu University, Chengdu 610041, China
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Chengdu 610041, China
| | - Runzhou Jiang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
- Medical Equipment Department, Xiangyang No.1 People's Hospital, Xiangyang 441000, China
| | - Donna Y Chen
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, United States
| | - Andrew Michael
- Duke Institute for Brain Sciences, Duke University, Durham, NC 27708, United States
| | - Chun Meng
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Bharat Biswal
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, United States
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10
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Jiang W, Cai L, Wang Z. Common hyper-entropy patterns identified in nicotine smoking, marijuana use, and alcohol use based on uni-drug dependence cohorts. Med Biol Eng Comput 2023; 61:3159-3166. [PMID: 37718388 PMCID: PMC10842973 DOI: 10.1007/s11517-023-02932-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 09/07/2023] [Indexed: 09/19/2023]
Abstract
Substance use disorders present similar behaviors and psychopathologies related to impaired decision making/inhibition control and information processing, suggesting common alterations in frontal and limbic brain areas. To test this hypothesis, we identified three uni-substance use cohorts with dependence to only one type of substance from the Human Connectome Project: marijuana dependence, nicotine dependence, and alcohol dependence. Fifty-nine marijuana uses, 34 nicotine smokers, 35 alcohol drinkers, and their age and sex-matched non-substance use controls were identified. We used brain entropy mapping to probe brain alterations in substance use disorders. Compared to non-substance use individuals, all three substance use disorder cohorts had increased brain entropy. Marijuana dependence and nicotine dependence showed overlapped hyper-brain entropy in bilateral dorso-lateral prefrontal cortex, anterior cingulate cortex, and right insula. Hyper-brain entropy in marijuana dependence and alcohol dependence overlap in left insula, left doso-lateral prefrontal cortex, and posterior cingulate. Hyper-brain entropy in nicotine dependence and alcohol dependence overlap only in left dorso-lateral prefrontal cortex. Hyper-brain entropy in those areas was correlated with increased impulsivity or reduced inhibition control in substance use disorder but not in controls. Drug dependence is associated with hyper-brain entropy in the prefrontal cortex and the meso-limbic system, independent of a specific addictive drug. Brain entropy in this circuit provides a sensitive marker to detect brain and behavioral alterations in substance user disorders.
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Affiliation(s)
- Wenyu Jiang
- Department of Neurological Rehabilitation, Jiangbin Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Luhui Cai
- Department of Neurological Rehabilitation, Jiangbin Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Ze Wang
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, 670 W. Baltimore St, Baltimore, MD, 20201, USA.
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Camargo A, Mauro GD, Wang Z. Task-induced changes in brain entropy. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.28.23289255. [PMID: 37205436 PMCID: PMC10187354 DOI: 10.1101/2023.04.28.23289255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Entropy indicates irregularity of a dynamic system with higher entropy indicating higher irregularity and more transit states. In the human brain, regional entropy has been increasingly assessed using resting state fMRI. Response of regional entropy to task has been scarcely studied. The purpose of this study is to characterize task-induced regional brain entropy (BEN) alterations using the large Human Connectome Project (HCP) data. To control the potential modulation by the block-design, BEN of task-fMRI was calculated from the fMRI images acquired during the task conditions only and then compared to BEN of rsfMRI. Compared to resting state, task-performance unanimously induced BEN reduction in the peripheral cortical area including both the task activated regions and task non-specific regions such as the task negative area and BEN increase in the centric part of the sensorimotor and perception networks. Task control condition showed large residual task effects. After controlling the task non-specific effects using the control BEN vs task BEN comparison, regional BEN showed task specific effects in target regions.
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Affiliation(s)
- Aldo Camargo
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine
| | - Gianpaolo Del Mauro
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine
| | - Ze Wang
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine
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Fan S, Yu Y, Wu Y, Kai Y, Wang H, Chen Y, Zu M, Pang X, Tian Y. Altered brain entropy and functional connectivity patterns in generalized anxiety disorder patients. J Affect Disord 2023; 332:168-175. [PMID: 36972849 DOI: 10.1016/j.jad.2023.03.062] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 03/14/2023] [Accepted: 03/18/2023] [Indexed: 03/29/2023]
Abstract
BACKGROUND Generalized anxiety disorder (GAD) is a highly prevalent disease characterized by chronic, pervasive, and intrusive worry. Previous resting-state functional MRI (fMRI) studies on GAD have mainly focused on conventional static linear features. Entropy analysis of resting-state functional magnetic resonance imaging (rs-fMRI) has recently been adopted to characterize brain temporal dynamics in some neuropsychological or psychiatric diseases. However, the nonlinear dynamic complexity of brain signals has been rarely explored in GAD. METHODS We measured the approximate entropy (ApEn) and sample entropy (SampEn) of the resting-state fMRI data from 38 GAD patients and 37 matched healthy controls (HCs). The brain regions with significantly different ApEn and SampEn values between the two groups were extracted. Using these brain regions as seed points, we also investigated whether there are differences in whole brain resting-state function connectivity (RSFC) pattern between GADs and HCs. Correlation analysis was subsequently conducted to investigate the association between brain entropy, RSFC and the severity of anxiety symptoms. A linear support vector machine (SVM) was used to assess the discriminative power of BEN and RSFC features among GAD patients and HCs. RESULTS Compared to the HCs, patients with GAD showed increased levels of ApEn in the right angular cortex (AG) and increased levels of SampEn in the right middle occipital gyrus (MOG) as well as the right inferior occipital gyrus (IOG). Contrarily, compared to the HCs, patients with GAD showed decreased RSFC between the right AG and the right inferior parietal gyrus (IPG). The SVM-based classification model achieved 85.33 % accuracy (sensitivity: 89.19 %; specificity: 81.58 %; and area under the receiver operating characteristic curve: 0.9018). The ApEn of the right AG and the SVM-based decision value was positively correlated with the Hamilton Anxiety Scale (HAMA). LIMITATIONS This study used cross-sectional data and sample size was small. CONCLUSION Patients with GAD showed increased level of nonlinear dynamical complexity of ApEn in the right AG and decreased linear features of RSFC in the right IPG. Combining the linear and nonlinear features of brain signals may be used to effectively diagnose psychiatric disorders.
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Affiliation(s)
- Siyu Fan
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
| | - Yue Yu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
| | - Yue Wu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
| | - Yiao Kai
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
| | - Hongping Wang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
| | - Yue Chen
- Shanghai Fourth People's Hospital Affiliated to Tongji University, Shanghai 200081, China
| | - Meidan Zu
- Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, China
| | - Xiaonan Pang
- Department of Neurology, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, China.
| | - Yanghua Tian
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; The College of Mental Health and Psychological Sciences, Anhui Medical University, Hefei 230032, China; Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei 230032, China.
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Lehmann DJ, Elshorbagy A, Hurley MJ. Many Paths to Alzheimer's Disease: A Unifying Hypothesis Integrating Biological, Chemical, and Physical Risk Factors. J Alzheimers Dis 2023; 95:1371-1382. [PMID: 37694367 DOI: 10.3233/jad-230295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Sporadic Alzheimer's disease (AD) is a complex, multifactorial disease. We should therefore expect to find many factors involved in its causation. The known neuropathology seen at autopsy in patients dying with AD is not consistently seen in all patients with AD and is sometimes seen in patients without dementia. This suggests that patients follow different paths to AD, with different people having slightly different combinations of predisposing physical, chemical and biologic risk factors, and varying neuropathology. This review summarizes what is known of the biologic and chemical predisposing factors and features in AD. We postulate that, underlying the neuropathology of AD is a progressive failure of neurons, with advancing age or other morbidity, to rid themselves of entropy, i.e., the disordered state resulting from brain metabolism. Understanding the diverse causes of AD may allow the development of new therapies targeted at blocking the paths that lead to dementia in each subset of patients.
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Affiliation(s)
- Donald J Lehmann
- Oxford Project to Investigate Memory and Ageing (OPTIMA), Department of Pharmacology, University of Oxford, Oxford, UK
| | - Amany Elshorbagy
- Department of Pharmacology, University of Oxford, Oxford, UK
- Department of Physiology, Faculty of Medicine, University of Alexandria, Alexandria, Egypt
| | - Michael J Hurley
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK
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Guan S, Wan D, Zhao R, Canario E, Meng C, Biswal BB. The complexity of spontaneous brain activity changes in schizophrenia, bipolar disorder, and ADHD was examined using different variations of entropy. Hum Brain Mapp 2022; 44:94-118. [PMID: 36358029 PMCID: PMC9783493 DOI: 10.1002/hbm.26129] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Revised: 08/02/2022] [Accepted: 08/07/2022] [Indexed: 11/13/2022] Open
Abstract
Adult attention deficit/hyperactivity disorder (ADHD), schizophrenia (SCHZ), and bipolar disorder (BP) have common symptoms and differences, and the underlying neural mechanisms are still unclear. This article will thoroughly discuss the differences between ADHD, BP, and SCHZ (31 healthy control and 31 ADHD; 34 healthy control and 34 BP; 42 healthy control and 42 SCHZ) relative to healthy subjects in combination with three atlases (et al., the Brainnetome atlas, the Dosenbach atlas, the Power atlas) and seven entropies (et al., approximate entropy (ApEn), sample entropy (SaEn), permutation entropy (PeEn), fuzzy entropy (FuEn), differential entropy (DiffEn), range entropy (RaEn), and dispersion entropy (DispEn)), as well as the prominent significant brain regions, in the hope of giving information that is more suitable for analyzing different diseases' entropy. First, the reliability (et al., intraclass correlation coefficient [ICC]) of seven kinds of entropy is calculated and analyzed by using the MSC dataset (10 subjects and 100 sessions in total) and simulation data; then, seven types of entropy and multiscale entropy expanded based on seven kinds of entropy are used to explore the differences and brain regions of ADHD, BP, and SCHZ relative to healthy subjects; and finally, by verifying the classification performance of the seven information entropies on ADHD, BP, and SCHZ, the effectiveness of the seven entropy methods is evaluated through these three methods. The core brain regions that affect the classification are given, and DiffEn performed best on ADHD, SaEn for BP, and RaEn for SCHZ.
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Affiliation(s)
- Sihai Guan
- Key Laboratory of Electronic and Information EngineeringState Ethnic Affairs Commission, College of Electronic and Information, Southwest Minzu UniversityChengduChina
| | - Dongyu Wan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Rong Zhao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Edgar Canario
- Department of Biomedical EngineeringNew Jersey Institute of TechnologyNewarkNew JerseyUSA
| | - Chun Meng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Bharat B. Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina,Department of Biomedical EngineeringNew Jersey Institute of TechnologyNewarkNew JerseyUSA
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